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Face Recognition - April 2009
Interview Date: March 2010
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Patrick J. Flynn Patrick J. Flynn
From the Special Topic of Face Recognition

In our Special Topics analysis of face recognition research, the work of Dr. Patrick J. Flynn ranks at #4 by total cites and #3 by cites per paper, based on 29 papers cited a total of 681 times. His work also ranks in the top 1% among scientists publishing in the field of Computer Science in Essential Science IndicatorsSM from Thomson Reuters, and he also has Highly Cited Papers in the field of Engineering in the database.

Dr. Flynn hails from the University of Notre Dame, where he is Professor of Computer Science and Engineering, and Concurrent Professor of Electrical Engineering. He is also an Associate Editor of the journals IEEE Transactions on Image Processing and IEEE Transactions on Information Forensics and Security. He is also the Vice President for Conferences of the IEEE Biometrics Council.

Below, he talks with about his highly cited work as it relates to face recognition.

  Would you tell us a bit about your educational background and research experiences?

I have a B.S. degree in Electrical Engineering and an M.S. and Ph.D. in Computer Science, all from Michigan State University. I've been a professor for 19 years, and worked at three different universities during that time. My research work began as an undergraduate (helping to design systems to detect vital signs from a distance), and my graduate studies focused on computer vision and pattern recognition. Work early in my career involved multiple extensions to my thesis work on object recognition as well as projects in ultrasound imaging, computer graphics and visualization, and image coding/compression.

  How did you get involved in facial recognition research?

I began to work in biometrics in the summer of 2001 with Kevin Bowyer, co-director of my research group here at Notre Dame. Over the years, we have established a well-respected group that has made good contributions to the advances in biometrics research and technology. Our basic research activity is supplemented by a long-term effort to collect biometric samples from consenting subjects using an IRB-approved protocol.

"The research community has gained a broader and deeper understanding of the challenges to effective iris image matching and is now actively pushing to expand the envelope of viable recognition to include recognition of persons at some distance from the sensor, images with visible light instead of infrared illumination, and combining iris matching with other modes."

Over the last seven years, the group has collected in excess of 200,000 images and videos of faces, irises, and other sites imaged with a variety of sensors. Much of the data is made available to other research groups and companies working in the field, often as part of US government-sponsored evaluations of biometrics technology.

  One of your highly cited papers in our analysis is the 2006 Computer Vision and Image Understanding paper, "A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition" (Bowyer KW, Chang K, Flynn P, 101[1]: 1-15). Would you walk our readers through this paper and why you think it is garnering citations?

This paper surveys the state of the art, circa 2006, in face recognition using 3D shape instead of, or in addition to, 2D face photographs. Three-dimensional sensors of various types have been around for several decades, and there has been understandable interest to see whether human identification from 3D face shape is superior to, comparable to, or inferior to identification from 2D photographs. 3D face images are less prone to strong contamination by lighting changes than 2D face images, and face pose (head position and orientation) can be standardized using a 3D model. Both of these advantages can be exploited by face recognition systems.

The paper lists 25 systems that recognize faces from 3D information alone, and provides summary descriptions of the techniques. It also lists 11 systems that combine 3D and 2D face images to perform recognition. It then concludes with a listing of trends or key issues in this research area, including the needs for better 3D sensors, algorithms, and experimental methodology.

  A few of your recent papers involve iris recognition—would you talk a bit about this aspect of your work? The idea that machines can learn to detect gender from the iris is particularly intriguing—how is that done?

Iris recognition research has seen explosive growth over the past several years and our group has been able to contribute to this community of researchers in two ways, namely the provision of iris image data sets and our basic research results on aspects of iris recognition. The research community has gained a broader and deeper understanding of the challenges to effective iris image matching and is now actively pushing to expand the envelope of viable recognition to include recognition of persons at some distance from the sensor, images with visible light instead of infrared illumination, and combining iris matching with other modes. Kevin Bowyer's recent work on gender prediction from iris images began as an undergraduate student project, and is an exploration of data-mining concepts applied to geometric features extracted from the iris image.

  What are your hopes for progress in facial recognition research over the next decade?

Face recognition systems will continue to advance in capability. We have seen the emergence of face recognition in the consumer market through products such as Picasa from Google and iPhoto from Apple. In these products, face recognizers are used to automate portions of the tiresome task of photo album management. This software is clearly limited in scope, thus concerns about privacy do not apply (indeed, the major criticisms of these programs have related to poor performance on low-quality images). Additional applications of face recognition may pop up in the consumer space, and the research community may contribute directly to these.

Security applications will still continue to motivate a lot of face recognition research, particularly recognition of people in non-ideal situations where the imagery is uncontrolled or poor in quality in other ways. Face recognition in surveillance video is of strong interest and has also proven to be very challenging.

  What would you like the "take-away lesson" about your research to be?

The summary message is that there are numerous basic and applied science problems in the general area of biometrics, and many real and potential challenges to both high-quality performance and broad acceptance in society. Researchers have an obligation to consider both of these issues, and contribute in both areas if they can.

Patrick J. Flynn, Ph.D.
Department of Computer Science and Engineering
University of Notre Dame
Notre Dame, IN, USA

Patrick J. Flynn's current most-cited paper in Essential Science Indicators, with 736 cites:
Jain AK, Murty MN, Flynn PJ, "Data clustering: a review," ACM Comput. Serv. 31 (3): 264-323, September 1999. Source: Essential Science Indicators from Thomson Reuters.


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Special Topics : Face Recognition : Patrick J. Flynn Interview - Special Topic of Face Recognition